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Photo tourism: Exploring photo collections in 3D

by Noah Snavely, Steven M. Seitz, Richard Szeliski - In Proc. ACM SIGGRAPH , 2006
"... Figure 1: Our system takes unstructured collections of photographs such as those from online image searches (a) and reconstructs 3D points and viewpoints (b) to enable novel ways of browsing the photos (c). We present a system for interactively browsing and exploring large unstructured collections o ..."
Abstract - Cited by 677 (38 self) - Add to MetaCart
Figure 1: Our system takes unstructured collections of photographs such as those from online image searches (a) and reconstructs 3D points and viewpoints (b) to enable novel ways of browsing the photos (c). We present a system for interactively browsing and exploring large unstructured collections

Parametric Shape Analysis via 3-Valued Logic

by Mooly Sagiv, Thomas Reps, Reinhard Wilhelm , 2001
"... Shape Analysis concerns the problem of determining "shape invariants"... ..."
Abstract - Cited by 660 (79 self) - Add to MetaCart
Shape Analysis concerns the problem of determining "shape invariants"...

Using spin images for efficient object recognition in cluttered 3D scenes

by Andrew E. Johnson, Martial Hebert - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1999
"... We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that i ..."
Abstract - Cited by 571 (9 self) - Add to MetaCart
We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor

Resource Description Framework (RDF) Model and Syntax Specification

by Ora Lassila, Ralph R. Swick, World Wide, Web Consortium , 1998
"... This document is a revision of the public working draft dated 1998-08-19 incorporating suggestions received in review comments and further deliberations of the W3C RDF Model and Syntax Working Group. With the publication of this draft, the RDF Model and Syntax Specification enters "last call.&q ..."
Abstract - Cited by 922 (6 self) - Add to MetaCart
This document is a revision of the public working draft dated 1998-08-19 incorporating suggestions received in review comments and further deliberations of the W3C RDF Model and Syntax Working Group. With the publication of this draft, the RDF Model and Syntax Specification enters "last call

A Model of Investor Sentiment

by Nicholas Barberis, Andrei Shleifer, Robert Vishny - Journal of Financial Economics , 1998
"... Recent empirical research in finance has uncovered two families of pervasive regularities: underreaction of stock prices to news such as earnings announcements, and overreaction of stock prices to a series of good or bad news. In this paper, we present a parsimonious model of investor sentiment, or ..."
Abstract - Cited by 743 (28 self) - Add to MetaCart
, or of how investors form beliefs, which is consistent with the empirical findings. The model is based on psychological evidence and produces both underreaction and overreaction for a wide range of parameter values. � 1998 Elsevier Science S.A. All rights reserved. JEL classification: G12; G14

Additive Logistic Regression: a Statistical View of Boosting

by Jerome Friedman, Trevor Hastie, Robert Tibshirani - Annals of Statistics , 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
Abstract - Cited by 1719 (25 self) - Add to MetaCart
Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input

The Skill Content of Recent Technological Change: An Empirical Exploration

by David H. Autor, Frank Levy, Richard J. Murnane , 2000
"... Recent empirical and case study evidence documents a strong association between the adoption of computers and increased use of college educated or non-production workers. With few exceptions, the conceptual link explaining how computer technology complements skilled labor or substitutes for unskille ..."
Abstract - Cited by 607 (29 self) - Add to MetaCart
the assumption that routine and non-routine tasks are imperfect substitutes, the task framework implies measurable changes in the task content of employment. We examine these changes using representative samples of workers from 1960 to 1998 where individual characteristics are augmented with Dictionary

Predictive reward signal of dopamine neurons

by Wolfram Schultz - Journal of Neurophysiology , 1998
"... Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behav-J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical self-stimulation, and drugs ..."
Abstract - Cited by 717 (12 self) - Add to MetaCart
Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behav-J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical self-stimulation, and drugs

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

by Maria Fox, Derek Long , 2003
"... In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, ..."
Abstract - Cited by 601 (41 self) - Add to MetaCart
the challenges of application. The international planning competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated

The Digital Michelangelo Project: 3D Scanning of Large Statues

by Marc Levoy, Szymon Rusinkiewicz, Brian Curless, Matt Ginzton, Jeremy Ginsberg, Kari Pulli, David Koller, Sean Anderson, Jonathan Shade, Lucas Pereira, James Davis, Duane Fulk , 2000
"... We describe a hardware and software system for digitizing the shape and color of large fragile objects under non-laboratory conditions. Our system employs laser triangulation rangefinders, laser time-of-flight rangefinders, digital still cameras, and a suite of software for acquiring, aligning, merg ..."
Abstract - Cited by 488 (8 self) - Add to MetaCart
We describe a hardware and software system for digitizing the shape and color of large fragile objects under non-laboratory conditions. Our system employs laser triangulation rangefinders, laser time-of-flight rangefinders, digital still cameras, and a suite of software for acquiring, aligning, merging, and viewing scanned data. As a demonstration of this system, we digitized 10 statues by Michelangelo, including the well-known figure of David, two building interiors, and all 1,163 extant fragments of the Forma Urbis Romae, a giant marble map of ancient Rome. Our largest single dataset is of the David - 2 billion polygons and 7,000 color images. In this paper, we discuss the challenges we faced in building this system, the solutions we employed, and the lessons we learned. We focus in particular on the unusual design of our laser triangulation scanner and on the algorithms and software we developed for handling very large scanned models. CR Categories: I.2.10 [Artificial Intelligence]...
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